The Entity-Relationship Model
CSCD34- Data Management Systems. A. Vaisman
1
Overview of Database Design

Requirements Analysis: Understand what data will be
stored in the database, and the operations it will be subject to.

Conceptual Design: (ER Model is used at this stage.)
 What are the entities and relationships in the enterprise?
 What information about these entities and relationships
should we store in the database?
 What are the integrity constraints or business rules that hold?
 A database `schema’ in the ER Model can be represented
pictorially (ER diagrams).
 Can map an ER diagram into a relational schema.

Logical Design: Convert the conceptual database design into
the data model underlying the DBMS chosen for the
CSCD34Data Management Systems. A. Vaisman
application.
2
Overview of Database Design (cont.)

Schema Refinement: (Normalization) Check relational
schema for redundancies and anomalies.

Physical Database Design and Tuning: Consider typical
workloads and further refinement of the database design (v.g.
build indices).

Application and Security Design: Consider aspects of the
application beyond data. Methodologies like UML often used
for addressing the complete software development cycle.
CSCD34- Data Management Systems. A. Vaisman
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ER Model Basics
ssn
name
lot
Employees
Entity: Real-world object distinguishable
from other objects. An entity is described
using a set of attributes.
 Entity Set: A collection of entities of the same
kind. E.g., all employees.




All entities in an entity set have the same set of
attributes.
Each entity set has a key(a set of attributes uniquely
identifying an entity).
Each attribute has a domain.
CSCD34- Data Management Systems. A. Vaisman
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name
ER Model Basics (Contd.)
name
Employees


dname
lot
lot
Employees
since
ssn
ssn
did
Works_In
budget
Departments
supervisor
subordinate
Reports_To
Relationship: Association among two or more entities. E.g., Peter works in
Pharmacy department.
Relationship Set: Collection of similar relationships.
 An n-ary relationship set R relates n entity sets E1 ... En; each
relationship in R involves entities e1  E1, ..., en  En
 Same entity set could participate in different relationship sets, or in
different “roles” in same set.
 Relationship sets can also have descriptive attributes (e.g., the since
attribute of Works_In). A relationship is uniquely identified by
participating entities without reference to descriptive attributes.
CSCD34- Data Management Systems. A. Vaisman
5
Key Constraints
(a.k.a. Cardinality)


Consider Works_In
(in previous slide):
An employee can
work in many
departments; a dept
can have many
employees.
In contrast, each
dept has at most one
manager, according
to the key constraint
on Manages.
since
name
ssn
dname
lot
Employees
1-to-1
1-to Many
did
Manages
Many-to-1
budget
Departments
Many-to-Many
Constraints are IMPORTANT because they must be ENFORCED
when IMPLEMENTING the database
CSCD34- Data Management Systems. A. Vaisman
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Key Constraints
(ternary relationships)
name
Location
name
Each employee can work at
most in one department at
a single location
12-233
12-354
12-243
12-299
ssn
dname
lot
Employees
did
works_In
budget
Departments
D10
•
•
•
•
D12
D13
Rome
London
Paris
CSCD34- Data Management Systems. A. Vaisman
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Participation Constraints

Does every department have a manager?

If so, this is a participation constraint: the participation of
Departments in Manages is said to be total (vs. partial).
• Every Department MUST have at least an employee
• Every employee MUST work at least in one department
• There may exist employees managing no department
since
name
ssn
dname
did
lot
Employees
Manages
budget
Departments
Works_In
since
CSCD34- Data Management Systems. A. Vaisman
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Weak Entities

A weak entity can be identified uniquely only by considering
the primary key of another (owner) entity.



Owner entity set and weak entity set must participate in a one-tomany relationship set (one owner, many weak entities).
Weak entity sets must have total participation in this identifying
relationship set.
transac# is a discriminator within a group of transactions in an ATM.
address
atmID
since
transac#
amount
type
ATM
CSCD34- Data Management Systems. A. Vaisman
Transactions
9
name
ISA (`is a’) Hierarchies
ssn
lot
Employees
As
in C++, or other PLs,
attributes are inherited.
hourly_wages
hours_worked
ISA
If
we declare A ISA B, every A entity
is also considered to be a B entity.
contractid
Hourly_Emps


Contract_Emps
Overlap constraints: Can Joe be an Hourly_Emps as well as a
Contract_Emps entity? if so, specify => Hourly_Emps OVERLAPS
Contract_Emps.
Covering constraints: Does every Employees’ entity also have to be an
Hourly_Emps or a Contract_Emps entity?. If so, write Hourly_Emps AND
Contract_Emps COVER Employees.
» Reasons for using ISA:
To add descriptive attributes specific to a subclass.
To identify entities that participate in a relationship.
CSCD34- Data Management Systems. A. Vaisman
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name
ssn
Aggregation

Employees
Used when we have
to model a
relationship
involving (entity
sets and) a
relationship set.


Monitors
pid
pbudget
Projects
until
since
started_on
Aggregation allows us
to treat a relationship
set as an entity set
for purposes of
participation in
(other) relationships.
Employees are
assigned to monitor
SPONSORSHIPS.
CSCD34- Data Management Systems.
lot
dname
did
Sponsors
budget
Departments
 Aggregation vs. ternary relationship:
Monitors and Sponsors are distinct
relationships, with descriptive attributes of
their own.
 Also, can say that each sponsorship
is monitored by at most one employee (which
A. Vaisman
11
we cannot do with a ternary relationship).

Conceptual Design Using the ER Model

Design choices:




Should a concept be modeled as an entity or an
attribute?
Should a concept be modeled as an entity or a
relationship?
Identifying relationships: Binary or ternary?
Aggregation?
Constraints in the ER Model:


A lot of data semantics can (and should) be captured.
But some constraints cannot be captured in ER
diagrams.
CSCD34- Data Management Systems. A. Vaisman
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Entity vs. Attribute
Should address be an attribute of Employees or an
entity (connected to Employees by a relationship)?
 Depends upon the use we want to make of address
information, and the semantics of the data:

• If we have several addresses per employee, address
must be an entity (since attributes cannot be setvalued).
• If the structure (city, street, etc.) is important, e.g., we
want to retrieve employees in a given city, address
must be modeled as an entity (since attribute values
are atomic).
CSCD34- Data Management Systems. A. Vaisman
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Entity vs. Attribute (Contd.)
from
name


Works_In4 does not
allow an employee to
work in a department
for two or more periods (a
relationship is identified
by participating entities).
Similar to the problem of
wanting to record several
addresses for an employee:
We want to record several
values of the descriptive
attributes for each instance of
this relationship.
Accomplished by
introducing new entity set,
Duration.
ssn
to
dname
lot
did
Works_In4
Employees
budget
Departments
name
dname
ssn
CSCD34- Data Management Systems. A. Vaisman
lot
Employees
from
did
Works_In4
Duration
budget
Departments
to
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Entity vs. Relationship


First ER diagram OK if
a manager gets a
separate discretionary
budget for each dept.
What if a manager gets
a discretionary
budget that covers
all managed depts?


Redundancy: dbudget
stored for each dept
managed by manager.
Misleading: Suggests
dbudget associated with
department-mgr
combination.
since
name
ssn
dbudget
lot
Employees
dname
did
budget
Departments
Manages2
name
ssn
lot
dname
since
did
Employees
ISA
Managers
CSCD34- Data Management Systems. A. Vaisman
Manages2
dbudget
budget
Departments
This fixes the
problem!
15
Binary vs. Ternary Relationships
name
ssn
Employees

Suppose:
 A policy cannot
be owned by
more than one
employee.
 Every policy
must be owned
by some
employee.
 Dependent is a
weak entity set,
identified by
policiId.
pname
lot
Dependents
Covers
Bad design
age
Policies
policyid
cost
name
pname
ssn
lot
Dependents
Employees
CSCD34- Data Management Systems. A. Vaisman
age
Purchaser
Beneficiary
Better design
policyid
Policies
cost
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Binary vs. Ternary Relationships (Contd.)
Previous example illustrated a case when two
binary relationships were better than one ternary
relationship.
 An example in the other direction: a ternary
relation Contracts relates entity sets Parts,
Departments and Suppliers, and has descriptive
attribute qty. No combination of binary
relationships is an adequate substitute:


Although S “can-supply” P, D “needs” P, and D
“deals-with” S, all these do not imply that D has
agreed to buy P from S (because D could buy P from
another supplier).
CSCD34- Data Management Systems. A. Vaisman
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Summary of Conceptual Design

Conceptual design follows requirements analysis,


Yields a high-level description of data to be stored
ER model popular for conceptual design

Constructs are expressive, close to the way people think
about their applications.
Basic constructs: entities, relationships, and attributes
(of entities and relationships).
 Some additional constructs: weak entities, ISA
hierarchies, and aggregation.
 Note: There are many variations on ER model.

CSCD34- Data Management Systems. A. Vaisman
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Summary of ER (Contd.)

Several kinds of integrity constraints can be expressed
in the ER model: key constraints, participation
constraints, and overlap/covering constraints for ISA
hierarchies. Some foreign key constraints are also
implicit in the definition of a relationship set.


Some constraints (notably, functional dependencies) cannot be
expressed in the ER model.
Constraints play an important role in determining the best
database design for an enterprise.
CSCD34- Data Management Systems. A. Vaisman
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Summary of ER (Contd.)

ER design is subjective. There are often many
ways to model a given scenario! Analyzing
alternatives can be tricky, especially for a large
enterprise. Common choices include:


Entity vs. attribute, entity vs. relationship, binary or nary relationship, whether or not to use ISA hierarchies,
and whether or not to use aggregation.
Ensuring good database design: resulting
relational schema should be analyzed and refined
further. FD information and normalization
techniques are especially useful.
CSCD34- Data Management Systems. A. Vaisman
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